Landslide　susceptibility　evaluation　plays　an　important　role　in　disaster　prevention　and　reduction.　Feature-based　transfer　learning　(TL)　is　an　effective　method　for　solving　landslide　susceptibility　mapping　(LSM)　in　target　regions　with　no　available　samples.　However,　as　the　study　area　expands,　the　distribution　of　landslide　types　and　triggering　mechanisms　becomes　more　diverse,　leading　to　performance　degradation　in　models　relying　on　landslide　evaluation　knowledge　from　a　single　source　domain　due　to　domain　feature　shift.　To　address　this,　this　study　proposes　a　Multi-source　Domain　Adaptation　Convolutional　Neural　Network　(MDACNN),　which　combines　the　landslide　prediction　knowledge　learned　from　two　source　domains　to　perform　cross-regional　LSM　in　complex　large-scale　areas.　The　method　is　validated　through　case　studies　in　three　regions　located　in　southeastern　coastal　China　and　compared　with　single-source　domain　TL　models　(TCA-based　models).　The　results　demonstrate　that　MDACNN　effectively　integrates　transfer　knowledge　from　multiple　source　domains　to　learn　diverse　landslide-triggering　mechanisms,　thereby　significantly　reducing　prediction　bias　inherent　to　single-source　domain　TL　models,　achieving　an　average　improvement　of　16.58%　across　all　metrics.　Moreover,　the　landslide　susceptibility　maps　generated　by　MDACNN　accurately　quantify　the　spatial　distribution　of　landslide　risks　in　the　target　area,　providing　a　powerful　scientific　and　technological　tool　for　landslide　disaster　management　and　prevention.　(c)　2025　China　University　of　Geosciences　(Beijing)　and　Peking　University.　Published　by　Elsevier　B.V.　on　behalf　of　China　University　of　Geosciences　(Beijing).　This　is　an　open　access　article　under　the　CC　BY-NC-ND　license　(http://creativecommons.org/licenses/by-nc-nd/4.0/).